Pandas Profiling Report

Dataset statistics

Number of variables	37
Number of observations	132
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	38.3 KiB
Average record size in memory	297.0 B

Variable types

Categorical	18
Numeric	19

Alerts

`Farm.Name` has a high cardinality: 57 distinct values	High cardinality
`Producer` has a high cardinality: 62 distinct values	High cardinality
`Grading.Date` has a high cardinality: 60 distinct values	High cardinality
`Expiration` has a high cardinality: 60 distinct values	High cardinality
`Altitude` is highly correlated with `altitude_low_meters` and 2 other fields	High correlation
`Aroma` is highly correlated with `Flavor` and 6 other fields	High correlation
`Flavor` is highly correlated with `Aroma` and 6 other fields	High correlation
`Aftertaste` is highly correlated with `Aroma` and 6 other fields	High correlation
`Acidity` is highly correlated with `Aroma` and 6 other fields	High correlation
`Body` is highly correlated with `Aroma` and 6 other fields	High correlation
`Balance` is highly correlated with `Aroma` and 6 other fields	High correlation
`Uniformity` is highly correlated with `Clean.Cup`	High correlation
`Clean.Cup` is highly correlated with `Uniformity` and 1 other fields	High correlation
`Sweetness` is highly correlated with `Clean.Cup`	High correlation
`Cupper.Points` is highly correlated with `Aroma` and 6 other fields	High correlation
`Total.Cup.Points` is highly correlated with `Aroma` and 6 other fields	High correlation
`altitude_low_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`altitude_high_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`altitude_mean_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`Altitude` is highly correlated with `altitude_low_meters` and 2 other fields	High correlation
`Aroma` is highly correlated with `Flavor` and 5 other fields	High correlation
`Flavor` is highly correlated with `Aroma` and 5 other fields	High correlation
`Aftertaste` is highly correlated with `Aroma` and 5 other fields	High correlation
`Acidity` is highly correlated with `Aroma` and 5 other fields	High correlation
`Body` is highly correlated with `Aroma` and 5 other fields	High correlation
`Balance` is highly correlated with `Aroma` and 5 other fields	High correlation
`Uniformity` is highly correlated with `Clean.Cup` and 2 other fields	High correlation
`Clean.Cup` is highly correlated with `Uniformity` and 2 other fields	High correlation
`Sweetness` is highly correlated with `Uniformity` and 2 other fields	High correlation
`Cupper.Points` is highly correlated with `Total.Cup.Points`	High correlation
`Total.Cup.Points` is highly correlated with `Aroma` and 9 other fields	High correlation
`altitude_low_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`altitude_high_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`altitude_mean_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`Altitude` is highly correlated with `altitude_low_meters` and 2 other fields	High correlation
`Aroma` is highly correlated with `Flavor` and 1 other fields	High correlation
`Flavor` is highly correlated with `Aroma` and 6 other fields	High correlation
`Aftertaste` is highly correlated with `Flavor` and 4 other fields	High correlation
`Acidity` is highly correlated with `Flavor` and 2 other fields	High correlation
`Body` is highly correlated with `Flavor` and 3 other fields	High correlation
`Balance` is highly correlated with `Aroma` and 6 other fields	High correlation
`Uniformity` is highly correlated with `Clean.Cup`	High correlation
`Clean.Cup` is highly correlated with `Uniformity` and 1 other fields	High correlation
`Sweetness` is highly correlated with `Clean.Cup`	High correlation
`Cupper.Points` is highly correlated with `Flavor` and 3 other fields	High correlation
`Total.Cup.Points` is highly correlated with `Flavor` and 5 other fields	High correlation
`altitude_low_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`altitude_high_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`altitude_mean_meters` is highly correlated with `Altitude` and 2 other fields	High correlation
`Owner.1` is highly correlated with `Species` and 12 other fields	High correlation
`unit_of_measurement` is highly correlated with `Farm.Name` and 2 other fields	High correlation
`Harvest.Year` is highly correlated with `Farm.Name` and 3 other fields	High correlation
`Species` is highly correlated with `Owner.1` and 9 other fields	High correlation
`Owner` is highly correlated with `Owner.1` and 12 other fields	High correlation
`Farm.Name` is highly correlated with `Owner.1` and 15 other fields	High correlation
`Grading.Date` is highly correlated with `Owner.1` and 12 other fields	High correlation
`Variety` is highly correlated with `Owner.1` and 10 other fields	High correlation
`Processing.Method` is highly correlated with `Owner.1` and 9 other fields	High correlation
`Country.of.Origin` is highly correlated with `Owner.1` and 11 other fields	High correlation
`In.Country.Partner` is highly correlated with `Owner.1` and 9 other fields	High correlation
`Color` is highly correlated with `Farm.Name` and 2 other fields	High correlation
`Expiration` is highly correlated with `Owner.1` and 12 other fields	High correlation
`Region` is highly correlated with `Owner.1` and 11 other fields	High correlation
`Company` is highly correlated with `Owner.1` and 12 other fields	High correlation
`Producer` is highly correlated with `Owner.1` and 14 other fields	High correlation
`Bag.Weight` is highly correlated with `Owner.1` and 6 other fields	High correlation
`Species` is highly correlated with `Owner` and 10 other fields	High correlation
`Owner` is highly correlated with `Species` and 22 other fields	High correlation
`Country.of.Origin` is highly correlated with `Species` and 21 other fields	High correlation
`Farm.Name` is highly correlated with `Species` and 24 other fields	High correlation
`Company` is highly correlated with `Species` and 27 other fields	High correlation
`Altitude` is highly correlated with `Producer` and 3 other fields	High correlation
`Region` is highly correlated with `Species` and 28 other fields	High correlation
`Producer` is highly correlated with `Species` and 31 other fields	High correlation
`Number.of.Bags` is highly correlated with `Owner` and 22 other fields	High correlation
`Bag.Weight` is highly correlated with `Owner` and 16 other fields	High correlation
`In.Country.Partner` is highly correlated with `Owner` and 16 other fields	High correlation
`Harvest.Year` is highly correlated with `Owner` and 11 other fields	High correlation
`Grading.Date` is highly correlated with `Species` and 26 other fields	High correlation
`Owner.1` is highly correlated with `Species` and 22 other fields	High correlation
`Variety` is highly correlated with `Species` and 19 other fields	High correlation
`Processing.Method` is highly correlated with `Owner` and 15 other fields	High correlation
`Aroma` is highly correlated with `Owner` and 18 other fields	High correlation
`Flavor` is highly correlated with `Farm.Name` and 17 other fields	High correlation
`Aftertaste` is highly correlated with `Owner` and 19 other fields	High correlation
`Acidity` is highly correlated with `Farm.Name` and 17 other fields	High correlation
`Body` is highly correlated with `Owner` and 21 other fields	High correlation
`Balance` is highly correlated with `Owner` and 20 other fields	High correlation
`Uniformity` is highly correlated with `Flavor` and 8 other fields	High correlation
`Clean.Cup` is highly correlated with `Number.of.Bags` and 9 other fields	High correlation
`Sweetness` is highly correlated with `Species` and 17 other fields	High correlation
`Cupper.Points` is highly correlated with `Owner` and 20 other fields	High correlation
`Total.Cup.Points` is highly correlated with `Company` and 15 other fields	High correlation
`Moisture` is highly correlated with `Owner` and 9 other fields	High correlation
`Category.One.Defects` is highly correlated with `Grading.Date` and 1 other fields	High correlation
`Quakers` is highly correlated with `Region` and 4 other fields	High correlation
`Color` is highly correlated with `Owner` and 9 other fields	High correlation
`Category.Two.Defects` is highly correlated with `Farm.Name` and 8 other fields	High correlation
`Expiration` is highly correlated with `Species` and 26 other fields	High correlation
`unit_of_measurement` is highly correlated with `Farm.Name` and 6 other fields	High correlation
`altitude_low_meters` is highly correlated with `Altitude` and 4 other fields	High correlation
`altitude_high_meters` is highly correlated with `Altitude` and 4 other fields	High correlation
`altitude_mean_meters` is highly correlated with `Altitude` and 4 other fields	High correlation
`Moisture` has 20 (15.2%) zeros	Zeros
`Category.One.Defects` has 124 (93.9%) zeros	Zeros
`Quakers` has 86 (65.2%) zeros	Zeros
`Category.Two.Defects` has 26 (19.7%) zeros	Zeros

Reproduction

Analysis started	2022-01-07 10:59:26.044723
Analysis finished	2022-01-07 11:00:25.949817
Duration	59.91 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

Species
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	1.5%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Arabica	130
Robusta	2

Length

Max length	7
Median length	7
Mean length	7
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Arabica
2nd row	Arabica
3rd row	Arabica
4th row	Arabica
5th row	Arabica

Common Values

Value	Count	Frequency (%)
Arabica	130	98.5%
Robusta	2	1.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
arabica	130	98.5%
robusta	2	1.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Owner
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	24
Distinct (%)	18.2%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

juan luis alvarado romero	45
ipanema coffees	23
lin, che-hao krude 林哲豪	13
bismarck castro	11
consejo salvadoreño del café	7
Other values (19)	33

Length

Max length	41
Median length	22
Mean length	20.20454545
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8 ?
Unique (%)	6.1%

Sample

1st row	lin, che-hao krude 林哲豪
2nd row	lin, che-hao krude 林哲豪
3rd row	consejo salvadoreño del café
4th row	rodrigo soto
5th row	juan luis alvarado romero

Common Values

Value	Count	Frequency (%)
juan luis alvarado romero	45	34.1%
ipanema coffees	23	17.4%
lin, che-hao krude 林哲豪	13	9.8%
bismarck castro	11	8.3%
consejo salvadoreño del café	7	5.3%
ceca, s.a.	4	3.0%
exportadora atlantic, s.a.	3	2.3%
janny marlith torres	2	1.5%
gabriel barbara	2	1.5%
elsy reyes	2	1.5%
Other values (14)	20	15.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
juan	45	10.9%
alvarado	45	10.9%
romero	45	10.9%
luis	45	10.9%
ipanema	23	5.6%
coffees	23	5.6%
lin	13	3.2%
che-hao	13	3.2%
krude	13	3.2%
林哲豪	13	3.2%
Other values (54)	133	32.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Country.of.Origin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	13
Distinct (%)	9.8%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Guatemala	45
Brazil	26
Honduras	17
Taiwan	13
Costa Rica	8
Other values (8)	23

Length

Max length	28
Median length	9
Mean length	8.090909091
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	2.3%

Sample

1st row	Taiwan
2nd row	Taiwan
3rd row	El Salvador
4th row	Costa Rica
5th row	Guatemala

Common Values

Value	Count	Frequency (%)
Guatemala	45	34.1%
Brazil	26	19.7%
Honduras	17	12.9%
Taiwan	13	9.8%
Costa Rica	8	6.1%
El Salvador	7	5.3%
Nicaragua	5	3.8%
Uganda	4	3.0%
Indonesia	2	1.5%
India	2	1.5%
Other values (3)	3	2.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
guatemala	45	30.0%
brazil	26	17.3%
honduras	17	11.3%
taiwan	13	8.7%
costa	8	5.3%
rica	8	5.3%
el	7	4.7%
salvador	7	4.7%
nicaragua	5	3.3%
uganda	4	2.7%
Other values (8)	10	6.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Farm.Name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	57
Distinct (%)	43.2%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

fazenda capoeirnha	13
capoeirinha	10
la esmeralda	7
la esperanza	6
las merceditas	5
Other values (52)	91

Length

Max length	33
Median length	12
Mean length	14.06060606
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	33 ?
Unique (%)	25.0%

Sample

1st row	tsoustructive garden 鄒築園
2nd row	shi fang yuan 十方源
3rd row	monterrey
4th row	rio jorco
5th row	san diego buena vista

Common Values

Value	Count	Frequency (%)
fazenda capoeirnha	13	9.8%
capoeirinha	10	7.6%
la esmeralda	7	5.3%
la esperanza	6	4.5%
las merceditas	5	3.8%
los hicaques	5	3.8%
piamonte	5	3.8%
las cuchillas	5	3.8%
chapultepec	4	3.0%
las delicias	4	3.0%
Other values (47)	68	51.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
las	20	7.1%
la	17	6.0%
fazenda	13	4.6%
capoeirnha	13	4.6%
capoeirinha	10	3.6%
finca	9	3.2%
coffee	7	2.5%
esmeralda	7	2.5%
esperanza	6	2.1%
los	6	2.1%
Other values (99)	173	61.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Company
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	32
Distinct (%)	24.2%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

unex guatemala, s.a.	32
ipanema coffees	23
taiwan coffee laboratory	13
cigrah s.a de c.v	11
consejo salvadoreño del café	4
Other values (27)	49

Length

Max length	44
Median length	20
Mean length	19.24242424
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	12 ?
Unique (%)	9.1%

Sample

1st row	taiwan coffee laboratory
2nd row	taiwan coffee laboratory
3rd row	cafetalera del pacifico
4th row	panamerican coffee trading
5th row	waelti schoenfeld exportadores de cafe, s.a.

Common Values

Value	Count	Frequency (%)
unex guatemala, s.a.	32	24.2%
ipanema coffees	23	17.4%
taiwan coffee laboratory	13	9.8%
cigrah s.a de c.v	11	8.3%
consejo salvadoreño del café	4	3.0%
ceca,s.a.	4	3.0%
mercon guatemala s.a.	3	2.3%
exportadora atlantic s.a	3	2.3%
volcafe ltda.	3	2.3%
siembras vision, s.a.	3	2.3%
Other values (22)	33	25.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
s.a	59	15.9%
guatemala	35	9.4%
unex	32	8.6%
ipanema	23	6.2%
coffees	23	6.2%
coffee	19	5.1%
de	15	4.0%
taiwan	13	3.5%
laboratory	13	3.5%
cigrah	13	3.5%
Other values (63)	127	34.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Altitude
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	50
Distinct (%)	37.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3026.639697

Minimum	16.8
Maximum	190164
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	16.8
5-th percentile	650
Q1	941.5
median	1400
Q3	1700
95-th percentile	4000
Maximum	190164
Range	190147.2
Interquartile range (IQR)	758.5

Descriptive statistics

Standard deviation	16440.12152
Coefficient of variation (CV)	5.431806614
Kurtosis	131.0964156
Mean	3026.639697
Median Absolute Deviation (MAD)	400
Skewness	11.43084947
Sum	399516.44
Variance	270277595.7
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1700	11	8.3%
890	11	8.3%
4000	10	7.6%
1500	9	6.8%
1400	7	5.3%
1250	5	3.8%
934	5	3.8%
1200	4	3.0%
1450	4	3.0%
1850	4	3.0%
Other values (40)	62	47.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
16.8	1	0.8%
350	2	1.5%
518	3	2.3%
650	2	1.5%
680	1	0.8%
750	1	0.8%
800	1	0.8%
872	1	0.8%
890	11	8.3%
894	2	1.5%

Value	Count	Frequency (%)
190164	1	0.8%
4500	1	0.8%
4000	10	7.6%
3702	1	0.8%
3664	1	0.8%
3607	1	0.8%
3280	3	2.3%
2100	1	0.8%
1901.64	1	0.8%
1901	3	2.3%

Region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	44
Distinct (%)	33.3%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

south of minas	23
san marcos	11
comayagua	10
oriente	10
tarrazu	8
Other values (39)	70

Length

Max length	46
Median length	10
Mean length	12.86363636
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	15.2%

Sample

1st row	leye, alishan township, chiayi county
2nd row	dongshan dist., tainan city 臺南市東山區
3rd row	apaneca
4th row	tarrazu
5th row	acatenango

Common Values

Value	Count	Frequency (%)
south of minas	23	17.4%
san marcos	11	8.3%
comayagua	10	7.6%
oriente	10	7.6%
tarrazu	8	6.1%
santa rosa	7	5.3%
huehuetenango	4	3.0%
apaneca	4	3.0%
jalapa	3	2.3%
antigua	3	2.3%
Other values (34)	49	37.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
minas	24	8.9%
south	23	8.5%
of	23	8.5%
oriente	12	4.4%
san	11	4.1%
marcos	11	4.1%
comayagua	10	3.7%
tarrazu	8	3.0%
dist	7	2.6%
tainan	7	2.6%
Other values (56)	134	49.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Producer
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	62
Distinct (%)	47.0%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Ipanema Agricola	12
Ipanema Agricola S.A	11
JESUS RAMIREZ	7
ANGEL DE LEON	5
JORGE LEAL	5
Other values (57)	92

Length

Max length	61
Median length	16
Mean length	17.62878788
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	36 ?
Unique (%)	27.3%

Sample

1st row	FANG,ZHENG-LUN 方政倫
2nd row	Wang Chao Yung 王超永
3rd row	J.J. Borja Nathan
4th row	Johanna
5th row	JUAN BOCK

Common Values

Value	Count	Frequency (%)
Ipanema Agricola	12	9.1%
Ipanema Agricola S.A	11	8.3%
JESUS RAMIREZ	7	5.3%
ANGEL DE LEON	5	3.8%
JORGE LEAL	5	3.8%
Reinerio Zepeda	5	3.8%
OTTO BECKER	4	3.0%
Nahun Maldonado	4	3.0%
CHAPULTEPEC	4	3.0%
Martin Gutierrez	4	3.0%
Other values (52)	71	53.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
agricola	28	7.7%
ipanema	23	6.3%
s.a	19	5.2%
de	15	4.1%
ramirez	8	2.2%
jesus	7	1.9%
jorge	7	1.9%
el	6	1.6%
zepeda	5	1.4%
maldonado	5	1.4%
Other values (147)	242	66.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Number.of.Bags
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	29
Distinct (%)	22.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	190.25

Minimum	1
Maximum	550
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	1
5-th percentile	10
Q1	50
median	250
Q3	290
95-th percentile	320
Maximum	550
Range	549
Interquartile range (IQR)	240

Descriptive statistics

Standard deviation	134.5387015
Coefficient of variation (CV)	0.7071679451
Kurtosis	-0.9841090492
Mean	190.25
Median Absolute Deviation (MAD)	70
Skewness	0.04343515515
Sum	25113
Variance	18100.66221
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=29)

Value	Count	Frequency (%)
275	29	22.0%
320	25	18.9%
50	15	11.4%
25	9	6.8%
20	7	5.3%
10	5	3.8%
200	5	3.8%
100	4	3.0%
250	4	3.0%
15	3	2.3%
Other values (19)	26	19.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1	0.8%
3	1	0.8%
6	1	0.8%
8	2	1.5%
10	5	3.8%
15	3	2.3%
20	7	5.3%
25	9	6.8%
50	15	11.4%
80	1	0.8%

Value	Count	Frequency (%)
550	2	1.5%
450	1	0.8%
440	1	0.8%
377	1	0.8%
325	1	0.8%
320	25	18.9%
310	1	0.8%
305	1	0.8%
285	1	0.8%
275	29	22.0%

Bag.Weight
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	16
Distinct (%)	12.1%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

69 kg	77
60 kg	29
50 kg	5
20 kg	3
10 kg	3
Other values (11)	15

Length

Max length	8
Median length	5
Mean length	5.022727273
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8 ?
Unique (%)	6.1%

Sample

1st row	50 kg
2nd row	20 kg
3rd row	69 kg
4th row	69 kg
5th row	34 kg

Common Values

Value	Count	Frequency (%)
69 kg	77	58.3%
60 kg	29	22.0%
50 kg	5	3.8%
20 kg	3	2.3%
10 kg	3	2.3%
59 kg	3	2.3%
30 kg	2	1.5%
5 kg	2	1.5%
34 kg	1	0.8%
12000 kg	1	0.8%
Other values (6)	6	4.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
kg	132	50.0%
69	77	29.2%
60	29	11.0%
50	5	1.9%
20	3	1.1%
10	3	1.1%
59	3	1.1%
5	2	0.8%
30	2	0.8%
34	1	0.4%
Other values (7)	7	2.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

In.Country.Partner
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	10
Distinct (%)	7.6%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Asociacion Nacional Del Café	45
Brazil Specialty Coffee Association	26
Instituto Hondureño del Café	22
Specialty Coffee Association	17
Specialty Coffee Association of Costa Rica	8
Other values (5)	14

Length

Max length	42
Median length	28
Mean length	30.23484848
Min length	22

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	2.3%

Sample

1st row	Specialty Coffee Association
2nd row	Specialty Coffee Association
3rd row	Salvadoran Coffee Council
4th row	Specialty Coffee Association of Costa Rica
5th row	Asociacion Nacional Del Café

Common Values

Value	Count	Frequency (%)
Asociacion Nacional Del Café	45	34.1%
Brazil Specialty Coffee Association	26	19.7%
Instituto Hondureño del Café	22	16.7%
Specialty Coffee Association	17	12.9%
Specialty Coffee Association of Costa Rica	8	6.1%
Salvadoran Coffee Council	7	5.3%
Uganda Coffee Development Authority	4	3.0%
Centro Agroecológico del Café A.C.	1	0.8%
Tanzanian Coffee Board	1	0.8%
Yunnan Coffee Exchange	1	0.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
del	68	13.1%
café	68	13.1%
coffee	64	12.3%
specialty	51	9.8%
association	51	9.8%
asociacion	45	8.7%
nacional	45	8.7%
brazil	26	5.0%
instituto	22	4.2%
hondureño	22	4.2%
Other values (15)	57	11.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Harvest.Year
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	3.8%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

2016	56
2017	40
2015	22
2017 / 2018	12
2016 / 2017	2

Length

Max length	11
Median length	4
Mean length	4.742424242
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2015
2nd row	2016
3rd row	2016
4th row	2015
5th row	2015

Common Values

Value	Count	Frequency (%)
2016	56	42.4%
2017	40	30.3%
2015	22	16.7%
2017 / 2018	12	9.1%
2016 / 2017	2	1.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2016	58	36.2%
2017	54	33.8%
2015	22	13.8%
	14	8.8%
2018	12	7.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Grading.Date
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	60
Distinct (%)	45.5%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

October 20th, 2017	11
August 16th, 2016	9
June 1st, 2017	8
August 22nd, 2017	7
May 18th, 2016	6
Other values (55)	91

Length

Max length	20
Median length	16
Mean length	15.84090909
Min length	13

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	34 ?
Unique (%)	25.8%

Sample

1st row	May 18th, 2016
2nd row	May 18th, 2016
3rd row	June 26th, 2017
4th row	October 4th, 2016
5th row	June 1st, 2016

Common Values

Value	Count	Frequency (%)
October 20th, 2017	11	8.3%
August 16th, 2016	9	6.8%
June 1st, 2017	8	6.1%
August 22nd, 2017	7	5.3%
May 18th, 2016	6	4.5%
June 22nd, 2017	5	3.8%
August 23rd, 2017	5	3.8%
April 6th, 2017	4	3.0%
May 23rd, 2016	4	3.0%
September 8th, 2017	4	3.0%
Other values (50)	69	52.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2017	76	19.2%
2016	56	14.1%
august	32	8.1%
june	31	7.8%
may	17	4.3%
october	14	3.5%
20th	14	3.5%
22nd	13	3.3%
23rd	13	3.3%
16th	11	2.8%
Other values (32)	119	30.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Owner.1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	24
Distinct (%)	18.2%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Juan Luis Alvarado Romero	45
Ipanema Coffees	23
Lin, Che-Hao Krude 林哲豪	13
Bismarck Castro	11
Consejo Salvadoreño del Café	7
Other values (19)	33

Length

Max length	41
Median length	22
Mean length	20.20454545
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8 ?
Unique (%)	6.1%

Sample

1st row	Lin, Che-Hao Krude 林哲豪
2nd row	Lin, Che-Hao Krude 林哲豪
3rd row	Consejo Salvadoreño del Café
4th row	Rodrigo Soto
5th row	Juan Luis Alvarado Romero

Common Values

Value	Count	Frequency (%)
Juan Luis Alvarado Romero	45	34.1%
Ipanema Coffees	23	17.4%
Lin, Che-Hao Krude 林哲豪	13	9.8%
Bismarck Castro	11	8.3%
Consejo Salvadoreño del Café	7	5.3%
CECA, S.A.	4	3.0%
Exportadora Atlantic, S.A.	3	2.3%
Janny Marlith Torres	2	1.5%
Gabriel Barbara	2	1.5%
Elsy Reyes	2	1.5%
Other values (14)	20	15.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
juan	45	10.9%
alvarado	45	10.9%
romero	45	10.9%
luis	45	10.9%
ipanema	23	5.6%
coffees	23	5.6%
lin	13	3.2%
che-hao	13	3.2%
krude	13	3.2%
林哲豪	13	3.2%
Other values (54)	133	32.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Variety
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	9.1%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Bourbon	62
Caturra	33
Catuai	10
Typica	8
Yellow Bourbon	5
Other values (7)	14

Length

Max length	14
Median length	7
Mean length	7.045454545
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	2.3%

Sample

1st row	Sumatra
2nd row	Typica
3rd row	Bourbon
4th row	Catuai
5th row	Bourbon

Common Values

Value	Count	Frequency (%)
Bourbon	62	47.0%
Caturra	33	25.0%
Catuai	10	7.6%
Typica	8	6.1%
Yellow Bourbon	5	3.8%
Pacas	4	3.0%
Other	3	2.3%
SL14	2	1.5%
Mundo Novo	2	1.5%
Sumatra	1	0.8%
Other values (2)	2	1.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
bourbon	67	48.2%
caturra	33	23.7%
catuai	10	7.2%
typica	8	5.8%
yellow	5	3.6%
pacas	4	2.9%
other	3	2.2%
sl14	2	1.4%
mundo	2	1.4%
novo	2	1.4%
Other values (3)	3	2.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Processing.Method
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	3.0%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Washed / Wet	88
Natural / Dry	34
Pulped natural / honey	5
Other	5

Length

Max length	22
Median length	12
Mean length	12.37121212
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Pulped natural / honey
2nd row	Natural / Dry
3rd row	Washed / Wet
4th row	Washed / Wet
5th row	Washed / Wet

Common Values

Value	Count	Frequency (%)
Washed / Wet	88	66.7%
Natural / Dry	34	25.8%
Pulped natural / honey	5	3.8%
Other	5	3.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
	127	32.5%
washed	88	22.5%
wet	88	22.5%
natural	39	10.0%
dry	34	8.7%
pulped	5	1.3%
honey	5	1.3%
other	5	1.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Aroma
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	13
Distinct (%)	9.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.568409091

Minimum	7.08
Maximum	8.08
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	7.08
5-th percentile	7.294
Q1	7.5
median	7.58
Q3	7.67
95-th percentile	7.83
Maximum	8.08
Range	1
Interquartile range (IQR)	0.17

Descriptive statistics

Standard deviation	0.1681458361
Coefficient of variation (CV)	0.02221680067
Kurtosis	0.8308716819
Mean	7.568409091
Median Absolute Deviation (MAD)	0.09
Skewness	0.0576267271
Sum	999.03
Variance	0.02827302221
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
7.5	32	24.2%
7.58	26	19.7%
7.67	24	18.2%
7.42	16	12.1%
7.75	11	8.3%
7.83	5	3.8%
7.33	5	3.8%
7.92	4	3.0%
7.17	3	2.3%
7.25	3	2.3%
Other values (3)	3	2.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
7.08	1	0.8%
7.17	3	2.3%
7.25	3	2.3%
7.33	5	3.8%
7.42	16	12.1%
7.5	32	24.2%
7.58	26	19.7%
7.67	24	18.2%
7.75	11	8.3%
7.83	5	3.8%

Value	Count	Frequency (%)
8.08	1	0.8%
8	1	0.8%
7.92	4	3.0%
7.83	5	3.8%
7.75	11	8.3%
7.67	24	18.2%
7.58	26	19.7%
7.5	32	24.2%
7.42	16	12.1%
7.33	5	3.8%

Flavor
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	13
Distinct (%)	9.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.55530303

Minimum	6.58
Maximum	8
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	6.58
5-th percentile	7.08
Q1	7.5
median	7.58
Q3	7.69
95-th percentile	7.83
Maximum	8
Range	1.42
Interquartile range (IQR)	0.19

Descriptive statistics

Standard deviation	0.2179482374
Coefficient of variation (CV)	0.02884705438
Kurtosis	2.729616835
Mean	7.55530303
Median Absolute Deviation (MAD)	0.09
Skewness	-1.112027239
Sum	997.3
Variance	0.04750143419
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
7.5	30	22.7%
7.58	19	14.4%
7.67	19	14.4%
7.75	18	13.6%
7.83	11	8.3%
7.42	11	8.3%
7.33	8	6.1%
7.08	4	3.0%
7.25	4	3.0%
7.92	3	2.3%
Other values (3)	5	3.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6.58	1	0.8%
7	3	2.3%
7.08	4	3.0%
7.25	4	3.0%
7.33	8	6.1%
7.42	11	8.3%
7.5	30	22.7%
7.58	19	14.4%
7.67	19	14.4%
7.75	18	13.6%

Value	Count	Frequency (%)
8	1	0.8%
7.92	3	2.3%
7.83	11	8.3%
7.75	18	13.6%
7.67	19	14.4%
7.58	19	14.4%
7.5	30	22.7%
7.42	11	8.3%
7.33	8	6.1%
7.25	4	3.0%

Aftertaste
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	15
Distinct (%)	11.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.404393939

Minimum	6.33
Maximum	8
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	6.33
5-th percentile	7
Q1	7.25
median	7.42
Q3	7.58
95-th percentile	7.75
Maximum	8
Range	1.67
Interquartile range (IQR)	0.33

Descriptive statistics

Standard deviation	0.242210739
Coefficient of variation (CV)	0.03271175751
Kurtosis	2.328305482
Mean	7.404393939
Median Absolute Deviation (MAD)	0.16
Skewness	-0.8273271807
Sum	977.38
Variance	0.0586660421
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
7.58	20	15.2%
7.33	19	14.4%
7.5	18	13.6%
7.42	18	13.6%
7.25	16	12.1%
7.67	10	7.6%
7.17	6	4.5%
7.75	5	3.8%
7.08	5	3.8%
7.83	4	3.0%
Other values (5)	11	8.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6.33	1	0.8%
6.75	1	0.8%
6.92	4	3.0%
7	4	3.0%
7.08	5	3.8%
7.17	6	4.5%
7.25	16	12.1%
7.33	19	14.4%
7.42	18	13.6%
7.5	18	13.6%

Value	Count	Frequency (%)
8	1	0.8%
7.83	4	3.0%
7.75	5	3.8%
7.67	10	7.6%
7.58	20	15.2%
7.5	18	13.6%
7.42	18	13.6%
7.33	19	14.4%
7.25	16	12.1%
7.17	6	4.5%

Acidity
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	18
Distinct (%)	13.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.581136364

Minimum	6.25
Maximum	8.25
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	6.25
5-th percentile	7.1295
Q1	7.5
median	7.58
Q3	7.75
95-th percentile	7.92
Maximum	8.25
Range	2
Interquartile range (IQR)	0.25

Descriptive statistics

Standard deviation	0.2606967989
Coefficient of variation (CV)	0.03438756229
Kurtosis	5.025161266
Mean	7.581136364
Median Absolute Deviation (MAD)	0.16
Skewness	-1.31189194
Sum	1000.71
Variance	0.06796282096
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=18)

Value	Count	Frequency (%)
7.67	27	20.5%
7.5	20	15.2%
7.58	18	13.6%
7.75	14	10.6%
7.42	13	9.8%
7.83	10	7.6%
7.92	6	4.5%
7.33	6	4.5%
8	4	3.0%
7.17	4	3.0%
Other values (8)	10	7.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6.25	1	0.8%
6.83	1	0.8%
6.92	1	0.8%
7	2	1.5%
7.08	2	1.5%
7.17	4	3.0%
7.25	1	0.8%
7.33	6	4.5%
7.42	13	9.8%
7.5	20	15.2%

Value	Count	Frequency (%)
8.25	1	0.8%
8.08	1	0.8%
8	4	3.0%
7.92	6	4.5%
7.83	10	7.6%
7.75	14	10.6%
7.67	27	20.5%
7.58	18	13.6%
7.5	20	15.2%
7.42	13	9.8%

Body
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	13
Distinct (%)	9.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.537272727

Minimum	6.42
Maximum	8
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	6.42
5-th percentile	7.25
Q1	7.42
median	7.5
Q3	7.67
95-th percentile	7.8705
Maximum	8
Range	1.58
Interquartile range (IQR)	0.25

Descriptive statistics

Standard deviation	0.2269414657
Coefficient of variation (CV)	0.03010922836
Kurtosis	3.864733037
Mean	7.537272727
Median Absolute Deviation (MAD)	0.17
Skewness	-0.9416289089
Sum	994.92
Variance	0.05150242887
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
7.5	22	16.7%
7.67	21	15.9%
7.58	20	15.2%
7.42	20	15.2%
7.33	14	10.6%
7.83	12	9.1%
7.75	5	3.8%
7.25	5	3.8%
7.92	4	3.0%
7	4	3.0%
Other values (3)	5	3.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6.42	1	0.8%
7	4	3.0%
7.17	1	0.8%
7.25	5	3.8%
7.33	14	10.6%
7.42	20	15.2%
7.5	22	16.7%
7.58	20	15.2%
7.67	21	15.9%
7.75	5	3.8%

Value	Count	Frequency (%)
8	3	2.3%
7.92	4	3.0%
7.83	12	9.1%
7.75	5	3.8%
7.67	21	15.9%
7.58	20	15.2%
7.5	22	16.7%
7.42	20	15.2%
7.33	14	10.6%
7.25	5	3.8%

Balance
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	16
Distinct (%)	12.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.505075758

Minimum	6.08
Maximum	8.17
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	6.08
5-th percentile	7.044
Q1	7.42
median	7.5
Q3	7.67
95-th percentile	7.83
Maximum	8.17
Range	2.09
Interquartile range (IQR)	0.25

Descriptive statistics

Standard deviation	0.2538632685
Coefficient of variation (CV)	0.03382554377
Kurtosis	7.096435901
Mean	7.505075758
Median Absolute Deviation (MAD)	0.125
Skewness	-1.503936878
Sum	990.67
Variance	0.0644465591
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=16)

Value	Count	Frequency (%)
7.5	30	22.7%
7.58	21	15.9%
7.67	17	12.9%
7.42	15	11.4%
7.83	9	6.8%
7.75	8	6.1%
7.33	8	6.1%
7.25	8	6.1%
7.17	4	3.0%
7	3	2.3%
Other values (6)	9	6.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6.08	1	0.8%
6.92	3	2.3%
7	3	2.3%
7.08	1	0.8%
7.17	4	3.0%
7.25	8	6.1%
7.33	8	6.1%
7.42	15	11.4%
7.5	30	22.7%
7.58	21	15.9%

Value	Count	Frequency (%)
8.17	1	0.8%
8	2	1.5%
7.92	1	0.8%
7.83	9	6.8%
7.75	8	6.1%
7.67	17	12.9%
7.58	21	15.9%
7.5	30	22.7%
7.42	15	11.4%
7.33	8	6.1%

Uniformity
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	6
Distinct (%)	4.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	9.863484848

Minimum	6
Maximum	10
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	6
5-th percentile	9.33
Q1	10
median	10
Q3	10
95-th percentile	10
Maximum	10
Range	4
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.5257259392
Coefficient of variation (CV)	0.05330022272
Kurtosis	32.8847162
Mean	9.863484848
Median Absolute Deviation (MAD)	0
Skewness	-5.396970922
Sum	1301.98
Variance	0.2763877631
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
10	118	89.4%
9.33	9	6.8%
8.67	2	1.5%
6.67	1	0.8%
8	1	0.8%
6	1	0.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6	1	0.8%
6.67	1	0.8%
8	1	0.8%
8.67	2	1.5%
9.33	9	6.8%
10	118	89.4%

Value	Count	Frequency (%)
10	118	89.4%
9.33	9	6.8%
8.67	2	1.5%
8	1	0.8%
6.67	1	0.8%
6	1	0.8%

Clean.Cup
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	3.8%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

10.0	125
9.33	4
8.67	1
8.0	1
6.0	1

Length

Max length	4
Median length	4
Mean length	3.984848485
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	2.3%

Sample

1st row	10.0
2nd row	10.0
3rd row	10.0
4th row	10.0
5th row	10.0

Common Values

Value	Count	Frequency (%)
10.0	125	94.7%
9.33	4	3.0%
8.67	1	0.8%
8.0	1	0.8%
6.0	1	0.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
10.0	125	94.7%
9.33	4	3.0%
8.67	1	0.8%
8.0	1	0.8%
6.0	1	0.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Sweetness
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	6
Distinct (%)	4.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	9.896363636

Minimum	6
Maximum	10
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	6
5-th percentile	9.33
Q1	10
median	10
Q3	10
95-th percentile	10
Maximum	10
Range	4
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.4658854702
Coefficient of variation (CV)	0.04707643002
Kurtosis	42.70593986
Mean	9.896363636
Median Absolute Deviation (MAD)	0
Skewness	-6.098378696
Sum	1306.32
Variance	0.2170492713
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
10	122	92.4%
9.33	6	4.5%
8.67	1	0.8%
6	1	0.8%
7.92	1	0.8%
7.75	1	0.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6	1	0.8%
7.75	1	0.8%
7.92	1	0.8%
8.67	1	0.8%
9.33	6	4.5%
10	122	92.4%

Value	Count	Frequency (%)
10	122	92.4%
9.33	6	4.5%
8.67	1	0.8%
7.92	1	0.8%
7.75	1	0.8%
6	1	0.8%

Cupper.Points
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	20
Distinct (%)	15.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.498484848

Minimum	5.17
Maximum	8.5
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	5.17
5-th percentile	7
Q1	7.33
median	7.5
Q3	7.69
95-th percentile	7.92
Maximum	8.5
Range	3.33
Interquartile range (IQR)	0.36

Descriptive statistics

Standard deviation	0.4054071271
Coefficient of variation (CV)	0.05406520588
Kurtosis	14.9149913
Mean	7.498484848
Median Absolute Deviation (MAD)	0.17
Skewness	-2.927023597
Sum	989.8
Variance	0.1643549387
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
7.5	27	20.5%
7.58	16	12.1%
7.67	13	9.8%
7.33	12	9.1%
7.83	11	8.3%
7.75	11	8.3%
7.25	10	7.6%
7.42	9	6.8%
7	5	3.8%
7.92	5	3.8%
Other values (10)	13	9.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
5.17	1	0.8%
5.25	1	0.8%
6.17	1	0.8%
6.75	1	0.8%
6.83	1	0.8%
7	5	3.8%
7.08	1	0.8%
7.17	1	0.8%
7.25	10	7.6%
7.33	12	9.1%

Value	Count	Frequency (%)
8.5	1	0.8%
8.17	1	0.8%
8	4	3.0%
7.92	5	3.8%
7.83	11	8.3%
7.75	11	8.3%
7.67	13	9.8%
7.58	16	12.1%
7.5	27	20.5%
7.42	9	6.8%

Total.Cup.Points
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	56
Distinct (%)	42.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	82.33515152

Minimum	63.08
Maximum	86.58
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	63.08
5-th percentile	79.17
Q1	81.73
median	82.75
Q3	83.58
95-th percentile	84.42
Maximum	86.58
Range	23.5
Interquartile range (IQR)	1.85

Descriptive statistics

Standard deviation	2.263464036
Coefficient of variation (CV)	0.02749085894
Kurtosis	39.5598785
Mean	82.33515152
Median Absolute Deviation (MAD)	0.92
Skewness	-4.921454797
Sum	10868.24
Variance	5.123269443
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
82.75	10	7.6%
83.17	6	4.5%
83	5	3.8%
84.17	5	3.8%
83.67	5	3.8%
83.92	4	3.0%
81.67	4	3.0%
81.83	4	3.0%
81.92	4	3.0%
82.5	4	3.0%
Other values (46)	81	61.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
63.08	1	0.8%
77.33	1	0.8%
78	1	0.8%
78.83	1	0.8%
79.08	1	0.8%
79.17	3	2.3%
79.67	2	1.5%
79.75	1	0.8%
79.83	1	0.8%
80	1	0.8%

Value	Count	Frequency (%)
86.58	1	0.8%
84.83	1	0.8%
84.67	3	2.3%
84.5	1	0.8%
84.42	2	1.5%
84.33	1	0.8%
84.25	3	2.3%
84.17	5	3.8%
84.08	1	0.8%
84	1	0.8%

Moisture
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	6
Distinct (%)	4.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.09037878788

Minimum	0
Maximum	0.13
Zeros	20
Zeros (%)	15.2%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0.1
median	0.1
Q3	0.11
95-th percentile	0.12
Maximum	0.13
Range	0.13
Interquartile range (IQR)	0.01

Descriptive statistics

Standard deviation	0.03908115071
Coefficient of variation (CV)	0.4324150791
Kurtosis	1.576563491
Mean	0.09037878788
Median Absolute Deviation (MAD)	0.01
Skewness	-1.806844865
Sum	11.93
Variance	0.001527336341
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0.11	50	37.9%
0.1	41	31.1%
0	20	15.2%
0.12	12	9.1%
0.09	7	5.3%
0.13	2	1.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	20	15.2%
0.09	7	5.3%
0.1	41	31.1%
0.11	50	37.9%
0.12	12	9.1%
0.13	2	1.5%

Value	Count	Frequency (%)
0.13	2	1.5%
0.12	12	9.1%
0.11	50	37.9%
0.1	41	31.1%
0.09	7	5.3%
0	20	15.2%

Category.One.Defects
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	6
Distinct (%)	4.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3560606061

Minimum	0
Maximum	31
Zeros	124
Zeros (%)	93.9%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	31
Range	31
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	2.763261089
Coefficient of variation (CV)	7.760648164
Kurtosis	117.946336
Mean	0.3560606061
Median Absolute Deviation (MAD)	0
Skewness	10.64561031
Sum	47
Variance	7.635611844
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	124	93.9%
1	3	2.3%
2	2	1.5%
31	1	0.8%
6	1	0.8%
3	1	0.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	124	93.9%
1	3	2.3%
2	2	1.5%
3	1	0.8%
6	1	0.8%
31	1	0.8%

Value	Count	Frequency (%)
31	1	0.8%
6	1	0.8%
3	1	0.8%
2	2	1.5%
1	3	2.3%
0	124	93.9%

Quakers
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	8
Distinct (%)	6.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.7196969697

Minimum	0
Maximum	8
Zeros	86
Zeros (%)	65.2%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	2.45
Maximum	8
Range	8
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.338304692
Coefficient of variation (CV)	1.859539151
Kurtosis	10.31215613
Mean	0.7196969697
Median Absolute Deviation (MAD)	0
Skewness	2.85599572
Sum	95
Variance	1.791059449
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	86	65.2%
2	20	15.2%
1	19	14.4%
4	2	1.5%
5	2	1.5%
7	1	0.8%
3	1	0.8%
8	1	0.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	86	65.2%
1	19	14.4%
2	20	15.2%
3	1	0.8%
4	2	1.5%
5	2	1.5%
7	1	0.8%
8	1	0.8%

Value	Count	Frequency (%)
8	1	0.8%
7	1	0.8%
5	2	1.5%
4	2	1.5%
3	1	0.8%
2	20	15.2%
1	19	14.4%
0	86	65.2%

Color
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	2.3%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

Green	107
Bluish-Green	15
Blue-Green	10

Length

Max length	12
Median length	5
Mean length	6.174242424
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Green
2nd row	Green
3rd row	Blue-Green
4th row	Blue-Green
5th row	Green

Common Values

Value	Count	Frequency (%)
Green	107	81.1%
Bluish-Green	15	11.4%
Blue-Green	10	7.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
green	107	81.1%
bluish-green	15	11.4%
blue-green	10	7.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Category.Two.Defects
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	8.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.795454545

Minimum	0
Maximum	12
Zeros	26
Zeros (%)	19.7%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2
Q3	4
95-th percentile	7
Maximum	12
Range	12
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.407807391
Coefficient of variation (CV)	0.8613294733
Kurtosis	0.9619631762
Mean	2.795454545
Median Absolute Deviation (MAD)	2
Skewness	0.9473642596
Sum	369
Variance	5.797536433
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	26	19.7%
1	22	16.7%
3	20	15.2%
2	19	14.4%
4	15	11.4%
5	13	9.8%
6	7	5.3%
7	4	3.0%
8	4	3.0%
10	1	0.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	26	19.7%
1	22	16.7%
2	19	14.4%
3	20	15.2%
4	15	11.4%
5	13	9.8%
6	7	5.3%
7	4	3.0%
8	4	3.0%
10	1	0.8%

Value	Count	Frequency (%)
12	1	0.8%
10	1	0.8%
8	4	3.0%
7	4	3.0%
6	7	5.3%
5	13	9.8%
4	15	11.4%
3	20	15.2%
2	19	14.4%
1	22	16.7%

Expiration
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	60
Distinct (%)	45.5%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

October 20th, 2018	11
August 16th, 2017	9
June 1st, 2018	8
August 22nd, 2018	7
May 18th, 2017	6
Other values (55)	91

Length

Max length	20
Median length	16
Mean length	15.84090909
Min length	13

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	34 ?
Unique (%)	25.8%

Sample

1st row	May 18th, 2017
2nd row	May 18th, 2017
3rd row	June 26th, 2018
4th row	October 4th, 2017
5th row	June 1st, 2017

Common Values

Value	Count	Frequency (%)
October 20th, 2018	11	8.3%
August 16th, 2017	9	6.8%
June 1st, 2018	8	6.1%
August 22nd, 2018	7	5.3%
May 18th, 2017	6	4.5%
June 22nd, 2018	5	3.8%
August 23rd, 2018	5	3.8%
April 6th, 2018	4	3.0%
May 23rd, 2017	4	3.0%
September 8th, 2018	4	3.0%
Other values (50)	69	52.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2018	76	19.2%
2017	56	14.1%
august	32	8.1%
june	31	7.8%
may	17	4.3%
october	14	3.5%
20th	14	3.5%
22nd	13	3.3%
23rd	13	3.3%
16th	11	2.8%
Other values (32)	119	30.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

unit_of_measurement
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	1.5%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

m	115
ft	17

Length

Max length	2
Median length	1
Mean length	1.128787879
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	m
2nd row	m
3rd row	m
4th row	m
5th row	m

Common Values

Value	Count	Frequency (%)
m	115	87.1%
ft	17	12.9%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
m	115	87.1%
ft	17	12.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

altitude_low_meters
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	49
Distinct (%)	37.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4153.672952

Minimum	157.8864
Maximum	190164
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	157.8864
5-th percentile	650
Q1	941.5
median	1250
Q3	1573.75
95-th percentile	1901
Maximum	190164
Range	190006.1136
Interquartile range (IQR)	632.25

Descriptive statistics

Standard deviation	23164.90414
Coefficient of variation (CV)	5.576968724
Kurtosis	63.37764574
Mean	4153.672952
Median Absolute Deviation (MAD)	315.5
Skewness	8.02412018
Sum	548284.8296
Variance	536612783.8
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=49)

Value	Count	Frequency (%)
1700	11	8.3%
890	11	8.3%
1219.2	10	7.6%
1500	9	6.8%
1400	7	5.3%
934	5	3.8%
1250	5	3.8%
1450	4	3.0%
1850	4	3.0%
1200	4	3.0%
Other values (39)	62	47.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
157.8864	3	2.3%
168	1	0.8%
350	2	1.5%
650	2	1.5%
680	1	0.8%
750	1	0.8%
800	1	0.8%
872	1	0.8%
890	11	8.3%
894	2	1.5%

Value	Count	Frequency (%)
190164	2	1.5%
3280	3	2.3%
2100	1	0.8%
1901	3	2.3%
1850	4	3.0%
1800	2	1.5%
1750	1	0.8%
1700	11	8.3%
1650	2	1.5%
1645	1	0.8%

altitude_high_meters
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	49
Distinct (%)	37.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4153.672952

Minimum	157.8864
Maximum	190164
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	157.8864
5-th percentile	650
Q1	941.5
median	1250
Q3	1573.75
95-th percentile	1901
Maximum	190164
Range	190006.1136
Interquartile range (IQR)	632.25

Descriptive statistics

Standard deviation	23164.90414
Coefficient of variation (CV)	5.576968724
Kurtosis	63.37764574
Mean	4153.672952
Median Absolute Deviation (MAD)	315.5
Skewness	8.02412018
Sum	548284.8296
Variance	536612783.8
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=49)

Value	Count	Frequency (%)
1700	11	8.3%
890	11	8.3%
1219.2	10	7.6%
1500	9	6.8%
1400	7	5.3%
934	5	3.8%
1250	5	3.8%
1450	4	3.0%
1850	4	3.0%
1200	4	3.0%
Other values (39)	62	47.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
157.8864	3	2.3%
168	1	0.8%
350	2	1.5%
650	2	1.5%
680	1	0.8%
750	1	0.8%
800	1	0.8%
872	1	0.8%
890	11	8.3%
894	2	1.5%

Value	Count	Frequency (%)
190164	2	1.5%
3280	3	2.3%
2100	1	0.8%
1901	3	2.3%
1850	4	3.0%
1800	2	1.5%
1750	1	0.8%
1700	11	8.3%
1650	2	1.5%
1645	1	0.8%

altitude_mean_meters
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	49
Distinct (%)	37.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4153.672952

Minimum	157.8864
Maximum	190164
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.2 KiB

Quantile statistics

Minimum	157.8864
5-th percentile	650
Q1	941.5
median	1250
Q3	1573.75
95-th percentile	1901
Maximum	190164
Range	190006.1136
Interquartile range (IQR)	632.25

Descriptive statistics

Standard deviation	23164.90414
Coefficient of variation (CV)	5.576968724
Kurtosis	63.37764574
Mean	4153.672952
Median Absolute Deviation (MAD)	315.5
Skewness	8.02412018
Sum	548284.8296
Variance	536612783.8
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=49)

Value	Count	Frequency (%)
1700	11	8.3%
890	11	8.3%
1219.2	10	7.6%
1500	9	6.8%
1400	7	5.3%
934	5	3.8%
1250	5	3.8%
1450	4	3.0%
1850	4	3.0%
1200	4	3.0%
Other values (39)	62	47.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
157.8864	3	2.3%
168	1	0.8%
350	2	1.5%
650	2	1.5%
680	1	0.8%
750	1	0.8%
800	1	0.8%
872	1	0.8%
890	11	8.3%
894	2	1.5%

Value	Count	Frequency (%)
190164	2	1.5%
3280	3	2.3%
2100	1	0.8%
1901	3	2.3%
1850	4	3.0%
1800	2	1.5%
1750	1	0.8%
1700	11	8.3%
1650	2	1.5%
1645	1	0.8%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	Species	Owner	Country.of.Origin	Farm.Name	Company	Altitude	Region	Producer	Number.of.Bags	Bag.Weight	In.Country.Partner	Harvest.Year	Grading.Date	Owner.1	Variety	Processing.Method	Aroma	Flavor	Aftertaste	Acidity	Body	Balance	Uniformity	Clean.Cup	Sweetness	Cupper.Points	Total.Cup.Points	Moisture	Quakers	Color	Category.Two.Defects	Expiration	unit_of_measurement	altitude_low_meters	altitude_high_meters	altitude_mean_meters
0	Arabica	lin, che-hao krude 林哲豪	Taiwan	tsoustructive garden 鄒築園	taiwan coffee laboratory	1200.0	leye, alishan township, chiayi county	FANG,ZHENG-LUN 方政倫	20	50 kg	Specialty Coffee Association	2015	May 18th, 2016	Lin, Che-Hao Krude 林哲豪	Sumatra	Pulped natural / honey	8.00	8.00	8.00	8.25	8.00	8.17	10.0	10.0	10.0	8.17	86.58	0.00	0.0	Green	0	May 18th, 2017	m	1200.0	1200.0	1200.0
1	Arabica	lin, che-hao krude 林哲豪	Taiwan	shi fang yuan 十方源	taiwan coffee laboratory	350.0	dongshan dist., tainan city 臺南市東山區	Wang Chao Yung 王超永	10	20 kg	Specialty Coffee Association	2016	May 18th, 2016	Lin, Che-Hao Krude 林哲豪	Typica	Natural / Dry	7.92	7.58	7.83	7.83	7.83	7.83	10.0	10.0	10.0	8.00	84.83	0.00	0.0	Green	0	May 18th, 2017	m	350.0	350.0	350.0
2	Arabica	consejo salvadoreño del café	El Salvador	monterrey	cafetalera del pacifico	1350.0	apaneca	J.J. Borja Nathan	150	69 kg	Salvadoran Coffee Council	2016	June 26th, 2017	Consejo Salvadoreño del Café	Bourbon	Washed / Wet	7.83	7.83	7.50	8.00	7.83	7.67	10.0	10.0	10.0	8.00	84.67	0.00	0.0	Blue-Green	2	June 26th, 2018	m	1350.0	1350.0	1350.0
3	Arabica	rodrigo soto	Costa Rica	rio jorco	panamerican coffee trading	1150.0	tarrazu	Johanna	15	69 kg	Specialty Coffee Association of Costa Rica	2015	October 4th, 2016	Rodrigo Soto	Catuai	Washed / Wet	8.08	7.75	7.67	7.83	7.50	7.92	10.0	10.0	10.0	7.92	84.67	0.10	0.0	Blue-Green	2	October 4th, 2017	m	1150.0	1150.0	1150.0
4	Arabica	juan luis alvarado romero	Guatemala	san diego buena vista	waelti schoenfeld exportadores de cafe, s.a.	1600.0	acatenango	JUAN BOCK	120	34 kg	Asociacion Nacional Del Café	2015	June 1st, 2016	Juan Luis Alvarado Romero	Bourbon	Washed / Wet	7.75	7.83	7.58	8.00	7.92	7.75	10.0	10.0	10.0	7.83	84.67	0.10	0.0	Green	1	June 1st, 2017	m	1600.0	1600.0	1600.0
5	Arabica	ecom japan limited	Uganda	kawacom sipi project	kawacom uganda ltd	1750.0	eastern uganda	Kawacom Uganda Ltd	200	12000 kg	Uganda Coffee Development Authority	2016	March 14th, 2017	ECOM Japan Limited	SL14	Washed / Wet	7.92	7.75	7.67	7.75	7.83	7.75	10.0	10.0	10.0	7.83	84.50	0.11	0.0	Green	1	March 14th, 2018	m	1750.0	1750.0	1750.0
6	Arabica	max gurdian	Costa Rica	several farmers	beneficios volcafé costa rica	1300.0	tarrazu	Beneficio San Diego	275	69 kg	Specialty Coffee Association of Costa Rica	2016	March 23rd, 2017	Max Gurdian	Caturra	Washed / Wet	7.58	7.83	7.83	8.00	7.67	7.58	10.0	10.0	10.0	7.92	84.42	0.10	0.0	Blue-Green	3	March 23rd, 2018	m	1300.0	1300.0	1300.0
7	Arabica	aulia arif syahri	Indonesia	darmawi	pt. olam indonesia	1400.0	sumatra brastagi	Surbakti	200	30 kg	Specialty Coffee Association	2017	March 14th, 2017	Aulia Arif Syahri	Mandheling	Other	7.67	7.67	7.83	7.58	7.83	7.83	10.0	10.0	10.0	8.00	84.42	0.00	0.0	Blue-Green	3	March 14th, 2018	m	1400.0	1400.0	1400.0
8	Arabica	bismarck castro	Honduras	los hicaques	cigrah s.a de c.v	1500.0	central region	Reinerio Zepeda	275	69 kg	Instituto Hondureño del Café	2016	April 4th, 2016	Bismarck Castro	Catuai	Washed / Wet	7.58	7.92	7.58	7.75	7.92	7.75	10.0	10.0	10.0	7.83	84.33	0.11	1.0	Green	2	April 4th, 2017	m	1500.0	1500.0	1500.0
9	Arabica	lin, che-hao krude 林哲豪	Taiwan	馨晴咖啡 good mood coffee	taiwan coffee laboratory	1000.0	國姓鄉 guoshing township	黃美桃 Huang Mei Tao	10	5 kg	Specialty Coffee Association	2016	August 10th, 2017	Lin, Che-Hao Krude 林哲豪	Typica	Washed / Wet	7.92	7.75	7.75	7.67	7.67	7.83	10.0	10.0	10.0	7.67	84.25	0.12	0.0	Blue-Green	0	August 10th, 2018	m	1000.0	1000.0	1000.0

Last rows

	Species	Owner	Country.of.Origin	Farm.Name	Company	Altitude	Region	Producer	Number.of.Bags	Bag.Weight	In.Country.Partner	Harvest.Year	Grading.Date	Owner.1	Variety	Processing.Method	Aroma	Flavor	Aftertaste	Acidity	Body	Balance	Uniformity	Clean.Cup	Sweetness	Cupper.Points	Total.Cup.Points	Moisture	Category.One.Defects	Quakers	Color	Category.Two.Defects	Expiration	unit_of_measurement	altitude_low_meters	altitude_high_meters	altitude_mean_meters
122	Arabica	juan luis alvarado romero	Guatemala	las delicias	unex guatemala, s.a.	4000.0	santa rosa	OTTO BECKER	50	69 kg	Asociacion Nacional Del Café	2017	August 22nd, 2017	Juan Luis Alvarado Romero	Bourbon	Washed / Wet	7.33	7.42	7.33	7.42	7.50	7.42	8.67	9.33	9.33	7.42	79.17	0.10	0	0.0	Green	0	August 22nd, 2018	ft	1219.2	1219.2	1219.2
123	Arabica	janny marlith torres	Honduras	la bendicion	coffee planet corporation s.a	1650.0	ocotepeque	Jorge Alfredo Pinto	15	69 kg	Instituto Hondureño del Café	2017	July 27th, 2017	Janny Marlith Torres	Catuai	Washed / Wet	7.58	7.00	6.75	6.92	7.00	6.92	10.00	10.00	10.00	7.00	79.17	0.11	0	0.0	Green	5	July 27th, 2018	m	1650.0	1650.0	1650.0
124	Arabica	juan luis alvarado romero	Guatemala	chapultepec	unex guatemala, s.a.	1320.0	occidente	CHAPULTEPEC	250	69 kg	Asociacion Nacional Del Café	2016	May 23rd, 2016	Juan Luis Alvarado Romero	Bourbon	Washed / Wet	7.08	7.00	6.92	7.42	7.00	6.92	10.00	10.00	10.00	6.83	79.17	0.11	0	8.0	Green	6	May 23rd, 2017	m	1320.0	1320.0	1320.0
125	Arabica	juan luis alvarado romero	Guatemala	las merceditas	unex guatemala, s.a.	1700.0	san marcos	ANGEL DE LEON	50	69 kg	Asociacion Nacional Del Café	2017	August 23rd, 2017	Juan Luis Alvarado Romero	Bourbon	Washed / Wet	7.58	7.67	7.42	7.42	7.67	7.67	8.67	8.67	8.67	7.67	79.08	0.10	0	0.0	Green	1	August 23rd, 2018	m	1700.0	1700.0	1700.0
126	Arabica	exportadora atlantic, s.a.	Nicaragua	finca las marías	exportadora atlantic s.a	1100.0	jalapa	Teófilo Narváez	1	1 kg	Instituto Hondureño del Café	2016	May 22nd, 2017	Exportadora Atlantic, S.A.	Caturra	Other	7.17	7.00	6.92	7.00	7.00	7.00	10.00	10.00	10.00	6.75	78.83	0.10	0	2.0	Green	4	May 22nd, 2018	m	1100.0	1100.0	1100.0
127	Arabica	lin, che-hao krude 林哲豪	Taiwan	大鋤花間 (hoe vs. flower coffee farm)	taiwan coffee laboratory	650.0	台南市東山區( dongshan dist., tainan city)	林俊吉( Lin, Chun-Chi)	8	10 kg	Specialty Coffee Association	2016	June 6th, 2017	Lin, Che-Hao Krude 林哲豪	Yellow Bourbon	Washed / Wet	7.50	7.08	7.33	7.17	7.50	7.50	6.67	10.00	10.00	7.25	78.00	0.00	3	0.0	Green	4	June 6th, 2018	m	650.0	650.0	650.0
128	Arabica	ipanema coffees	Brazil	fazenda capoeirnha	ipanema coffees	890.0	south of minas	Ipanema Agricola S.A	320	60 kg	Brazil Specialty Coffee Association	2017 / 2018	October 20th, 2017	Ipanema Coffees	Bourbon	Natural / Dry	7.50	7.33	7.42	7.58	7.58	6.92	8.00	8.00	10.00	7.00	77.33	0.00	0	0.0	Green	3	October 20th, 2018	m	890.0	890.0	890.0
129	Arabica	exportadora atlantic, s.a.	Nicaragua	finca las marías	exportadora atlantic s.a	1100.0	jalapa	Teófilo Narváez	550	69 kg	Instituto Hondureño del Café	2016	June 6th, 2017	Exportadora Atlantic, S.A.	Caturra	Other	7.25	6.58	6.33	6.25	6.42	6.08	6.00	6.00	6.00	6.17	63.08	0.13	1	0.0	Green	5	June 6th, 2018	m	1100.0	1100.0	1100.0
130	Robusta	nishant gurjer	India	sethuraman estate	kaapi royale	1000.0	chikmagalur karnataka	Sethuraman Estate Kaapi Royale	320	60 kg	Specialty Coffee Association	2015	August 17th, 2016	Nishant Gurjer	Other	Washed / Wet	7.67	7.75	7.58	7.83	7.83	8.00	10.00	10.00	7.92	7.92	82.50	0.09	0	0.0	Green	0	August 17th, 2017	m	1000.0	1000.0	1000.0
131	Robusta	nishant gurjer	India	sethuraman estate kaapi royale	kaapi royale	1000.0	chikmagalur karnataka	Sethuraman Estate Kaapi Royale	320	60 kg	Specialty Coffee Association	2015	August 23rd, 2016	Nishant Gurjer	Other	Natural / Dry	7.67	7.83	7.75	7.50	7.75	7.58	10.00	10.00	7.75	7.75	81.58	0.11	0	0.0	Green	0	August 23rd, 2017	m	1000.0	1000.0	1000.0

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories